Have you seen this preprint on 'Visualization of Biomedical Data'? Authors outline initiatives aimed at fostering improvements to how we see and think about our data https://t.co/Pe2K6s3M8q https://t.co/Ums2ARq08n

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Abstract

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.

Author Comment

This work was accepted for publication in the inaugural issue of Annual Review of Biomedical Data.

Data Deposition

The following information was supplied regarding data availability:

Code and data are not the primary focus of this review. Several of the authors employed scripts and public data to create visualisations but the figures in the manuscript are the product of manual adaptation to illustrate limitations of existing tools and need for more effective biological data visualisation systems (which is the topic of this review).

Funding

JB Procter was supported by the UK Biotechnology and Biological Sciences Research Council [grant number BB/L020742/1]. JR Swedlow and WJ Moore are supported by the Wellcome Trust [grant number 202908/Z/16/Z] and UK Biotechnology and Biological Sciences Research Council [grant number BB/P027032/1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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